Questions to ask:
Where do people tend to die (urban, rural, mixed)
distance to clinics, pharmacies, hospitals
correlation between deaths and admissions to these distances?
treat$Admissions[is.na(treat$Admissions)] <- 0
gg <- ggplot(treat, aes(y=Admissions, x=FiscalYear))
gg <- gg + geom_bar(stat="identity")
gg <- gg + facet_wrap(~Town, ncol=4, scale="free_x")
#gg <- gg + facet_wrap(~state, scale="free", ncol=5)
#gg <- gg + scale_x_continuous(limits=c(2012,2016), breaks=c(2012,2016),
# labels=c("2012", "2016"))
gg <- gg + labs(x=NULL, y=NULL,
title="Opioid Related Treatment Admissions by Town",
subtitle="",
caption="Department of Mental Health and Addiction Services")
gg <- gg + theme_minimal(base_family="Lato Regular")
gg <- gg + theme(panel.grid.major.y=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(plot.title=element_text(family="Lato Black"))
gg
# gg <- ggplot(treat, aes(y=Unduplicated.Clients, x=FiscalYear))
# gg <- gg + geom_bar(stat="identity")
# gg <- gg + facet_wrap(~Town, ncol=4, scale="free_x")
# #gg <- gg + facet_wrap(~state, scale="free", ncol=5)
# #gg <- gg + scale_x_continuous(limits=c(2012,2016), breaks=c(2012,2016),
# # labels=c("2012", "2016"))
# gg <- gg + labs(x=NULL, y=NULL,
# title="Opioid Related Treatment Admissions (unduplicated clients) by Town",
# subtitle="",
# caption="Department of Mental Health and Addiction Services")
# gg <- gg + theme_minimal(base_family="Lato Regular")
# gg <- gg + theme(panel.grid.major.y=element_blank())
# gg <- gg + theme(panel.grid.minor=element_blank())
# gg <- gg + theme(plot.title=element_text(family="Lato Black"))
# gg
treat$Admissions[is.na(treat$Admissions)] <- 0
treat <- ctpopulator(Town, treat)
## [1] "Checking to see if names match..."
treat$per_capita_a <- round(treat$Admissions/treat$pop2013*1000, 2)
gg <- ggplot(treat, aes(y=per_capita_a, x=FiscalYear))
gg <- gg + geom_bar(stat="identity")
gg <- gg + facet_wrap(~Town, ncol=4, scale="free_x")
#gg <- gg + facet_wrap(~state, scale="free", ncol=5)
#gg <- gg + scale_x_continuous(limits=c(2012,2016), breaks=c(2012,2016),
# labels=c("2012", "2016"))
gg <- gg + labs(x=NULL, y=NULL,
title="Opioid Related Treatment Admissions per 1,000 residents by Town",
subtitle="",
caption="Department of Mental Health and Addiction Services")
gg <- gg + theme_minimal(base_family="Lato Regular")
gg <- gg + theme(panel.grid.major.y=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(plot.title=element_text(family="Lato Black"))
gg
town_shape <- readOGR(dsn="maps", layer="ctgeo")
## OGR data source with driver: ESRI Shapefile
## Source: "maps", layer: "ctgeo"
## with 169 features
## It has 6 fields
town_shape_df <- fortify(town_shape, region="NAME10")
names(treat)[names(treat) == 'Town'] <- 'id'
treat$id <- str_to_title(treat$id)
town_map <- left_join(town_shape_df, treat)
gg <- ggplot(town_map, aes(long,lat, group=group, fill=per_capita_a))
gg <- gg + geom_polygon()
gg <- gg + geom_path(color = "grey73")
gg <- gg + coord_equal()
gg <- gg + facet_wrap(~FiscalYear, ncol=2)
gg <- gg + scale_fill_gradient(low="grey73", high="blue")
gg <- gg + labs(x=NULL, y=NULL, title="Opioid Related Treatment Admissions by town",
subtitle="Per 1,000 residents",
caption="SOURCE: Department of Mental Health and Addiction Services\nAndrew Ba Tran/TrendCT.org")
gg <- gg + theme_bw(base_family="Lato Regular")
gg <- gg + theme(text = element_text(size=16))
gg <- gg + theme(panel.grid.major=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(axis.ticks=element_blank())
gg <- gg + theme(axis.text.x=element_blank())
gg <- gg + theme(axis.text.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold", family="Lato Black", size=22))
gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12)))
gg <- gg + theme(plot.caption=element_text(size=12, margin=margin(t=10, r=80), color="#7a7d7e"))
gg <- gg + theme(plot.margin = unit(c(1, 1, 1, 1), "lines"))
gg
tab1 <- treat %>%
select(id, FiscalYear, per_capita_a) %>%
spread(FiscalYear, per_capita_a) %>%
arrange(-`2016`)
kable(head(tab1, 10))
| id | 2012 | 2013 | 2014 | 2015 | 2016 |
|---|---|---|---|---|---|
| Canaan | 28.75 | 24.39 | 20.91 | 32.23 | 31.36 |
| Windham | 16.87 | 20.26 | 22.36 | 27.53 | 25.08 |
| Torrington | 10.40 | 13.37 | 16.06 | 19.94 | 23.74 |
| New London | 16.09 | 17.40 | 23.27 | 18.92 | 20.41 |
| Hartford | 18.17 | 17.59 | 18.60 | 20.34 | 19.12 |
| Sharon | 10.47 | 13.00 | 14.08 | 15.53 | 18.06 |
| Griswold | 10.29 | 12.54 | 15.30 | 15.72 | 16.97 |
| Waterbury | 12.08 | 12.47 | 12.90 | 16.16 | 16.66 |
| Hampton | 5.51 | 14.88 | 6.06 | 13.77 | 16.53 |
| Sprague | 11.72 | 11.72 | 16.08 | 15.07 | 16.08 |
source("urban_rural_mixed.R")
town_count <- town_count[c("NAME10", "Type", "perc_urban")]
colnames(town_count) <- c("id", "town_type", "perc_urban")
tab2 <- treat %>%
select(id, FiscalYear, Admissions, pop2013, per_capita_a)
tab2_joined <- left_join(tab2, town_count)
gg <- ggplot(tab2_joined, aes(x=town_type, y=Admissions,fill=town_type))
gg <- gg + geom_boxplot()
gg <- gg + facet_wrap(~FiscalYear, ncol=5)
gg <- gg + labs(x=NULL, y="Admissions", title="Overall opioid-related checkins in Connecticut",
subtitle="Per 1,000 residents",
caption="SOURCE: Department of Mental Health and Addiction Services\nAndrew Ba Tran/TrendCT.org")
gg <- gg + theme_bw(base_family="Lato Regular")
gg <- gg + theme(text = element_text(size=16))
gg <- gg + theme(panel.grid.major=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(axis.text.x=element_text(angle=90, vjust=0.4,hjust=1))
#gg <- gg + theme(axis.ticks=element_blank())
#gg <- gg + theme(axis.text.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold", family="Lato Black", size=22))
gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12)))
gg <- gg + theme(plot.caption=element_text(size=12, margin=margin(t=10, r=80), color="#7a7d7e"))
gg <- gg + theme(plot.margin = unit(c(1, 1, 1, 1), "lines"))
gg
p <- ggplot(tab2_joined, aes(x=town_type, y=per_capita_a,fill=town_type))
gg <- gg + geom_boxplot()
gg <- gg + facet_grid(.~FiscalYear)
gg <- gg + labs(x=NULL, y="Admissions per capita", title="Overall opioid-related checkins in Connecticut",
subtitle="Per 1,000 residents",
caption="SOURCE: Department of Mental Health and Addiction Services\nAndrew Ba Tran/TrendCT.org")
gg <- gg + theme_bw(base_family="Lato Regular")
gg <- gg + theme(text = element_text(size=16))
gg <- gg + theme(panel.grid.major=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(axis.text.x=element_text(angle=90, vjust=0.4,hjust=1))
#gg <- gg + theme(axis.ticks=element_blank())
#gg <- gg + theme(axis.text.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold", family="Lato Black", size=22))
gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12)))
gg <- gg + theme(plot.caption=element_text(size=12, margin=margin(t=10, r=80), color="#7a7d7e"))
gg <- gg + theme(plot.margin = unit(c(1, 1, 1, 1), "lines"))
gg